A Text Clustering Algorithm Based on Simplified Cluster Hypothesis

被引：0
|
作者：
Sun Yuan [1 ,2 ]
Guo Wenbin [1 ,2 ]
机构：
[1] Minzu Univ China, Sch Informat Engn, Beijing 100081, Peoples R China
[2] Natl Language Resource & Monitoring Res Ctr, Minor Languages Branch, Beijing 100081, Peoples R China
来源：
2013 2ND INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION AND MEASUREMENT, SENSOR NETWORK AND AUTOMATION (IMSNA) | 2013年
关键词：
component; VSM; feature vector optimization; text similarity; text clustering;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
How to quickly and efficiently determine the subject category from a large amount of text is becoming an important challenge in text clustering. In this paper, One-Next text clustering algorithm based on the simplified cluster hypothesis is proposed. Meanwhile, a feature vector optimization method using grading feature vector extraction method is designed. Finally, the experimental results show that this method can get a high precession and F value, and the algorithm complexity is lower than other text clustering methods.
引用
收藏
页码：412 / 415
页数：4
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